System and machine implemented method for adaptive collaborative matching

ABSTRACT

An adaptive collaborative platform applies various machine learning techniques to correlate potential purchasers with high-value articles of property that may be of interest. Attributes, characteristics, preferences, and the like of a potential purchaser are scored against attributes and features of articles. The platform learns from interaction by the agents and the potential purchasers and adapts to become more attuned to the desires and lifestyle of purchasers and to gain more and more pertinent information from the listing agents regarding high-value articles, so as to ultimately to arrive at a better match between a high value article for sale and a likely purchaser.

RELATED APPLICATION

The present application relates to and claims the benefit of priority toU.S. Provisional Patent Application Ser. No. 62/774,769 filed 3 Dec.2018, and is a continuation in part of U.S. patent application Ser. No.16/555,168 filed 29 Aug. 2019, both of which are hereby incorporated byreference in their entirety for all purposes as if fully set forthherein.

BACKGROUND OF THE INVENTION Field of the Invention

Embodiments of the present invention relate, in general, to an adaptivecollaborative data matching platform and more particularly to a systemand associated methodology for adaptively matching normalized productdata with purchaser affinities.

Relevant Background

Traditionally, high value assets or articles such as an estate, yacht,fine art or the like, are marketed locally among a discrete audience orthrough a specific “broadcast” network. An agent, brokerage, or firmhaving a local presence is engaged by one or more owners of the asset tofacilitate a transaction. Information of a new property for sale,whether the property is real estate, art, jewelry, automobiles or thelike, is published using local media, networks and any other means bywhich to advertise the attributes of the property to potential buyers.Typically, each asset owner individually engages a single firm to listtheir property and place it on the market. And while it is implied, oreven explicitly stated, that the listing of the property for sale isshared to numerous other brokerages via publicly available informationsources such as the Internet and other publications, the reality is thatno true system of collaboration exists.

Concurrently, those interested in buying high value item contact agents,brokerages or firms to review any inventory of properties of which theymay be aware, and to leverage their knowledge of the market with respectto particular types of properties that may be for sale. Those agents orfirms with a network of information can bring forth more opportunities.In theory every firm is aware of every property offered by sale of allother firms in a market arena or in a network so as to provide eachpotential buyer with a comprehensive list of opportunities. Again,reality is far different.

Information with respect to available assets, their attributes andcharacteristics and data related to potential buyers is not universallyshared. As a result, information, of both assets being sold and ofpotential buyers of such assets, is largely siloed and unstructured.Firms first attempt to sell a property or an asset known only to them tothose potential buyers with whom they have a relationship. If a sale isnot consummated internally, the agent may examine the market and networkof colleagues, using her understanding of the client's preferences, toidentify property that may be of interest to the client. In most casesthe agent calls friends, colleagues and looks at publicly availableinformation to identify property that they feel meet their client'sinterest, but the process is haphazard at best. No central repository ofstructured data exists from which the agent can draw or submitinformation as to the buyer, their lifestyle or preference. It isfundamentally up to the agent to attempt to understand their client'sdesires and match them with inventory of which they are aware.

Assets unknown to the agent remain undisclosed to the client. Moreover,the agent's ability to identify items that may be attractive to theclient are constrained by the client's ability and willingness to conveysuch preferences and/or the agent's willingness and ability to discoverthem. Lastly, the market for the sale of high-value items remainslargely a local market. Agents in San Francisco are unlikely to have anextensive and current knowledge of an offering in New York. Agents inNew York are unaware of what may be available in Paris and a client inBeijing looking for something that may be available in Los Angeles wouldfind little assistance from the local Beijing firm.

The compartmentalized nature of such high value asset markets andproprietary client information deters collaboration. Information withrespect to items for sale and clients willing to purchase such assets,especially in the high-net worth arena, are not openly shared, nor isthere any means by which to capture feedback to refine the searchparameters and match a client seeking a particular type of property withan article that meets that client's demands.

A need therefore exists to provide a system and associated methodologyto collect, normalize, and encrypt data and present that data in anadaptive collaborative environment accessible to a wide audience ofqualified professionals or interested qualified parties. A need furtherexists to collect and refine client interests', attributes, data(structured and unstructured), and the like, and thereafter match thoseinterests and attributes with elements of current and potentialofferings. Lastly, a need exists for such a system to learn from userinput and to refine its matching process so as to be adaptive. These andother deficiencies of the prior art are addressed by one or moreembodiments of the present invention.

Additional advantages and novel features of this invention shall be setforth in part in the description that follows, and in part will becomeapparent to those skilled in the art upon examination of the followingspecification or may be learned by the practice of the invention. Theadvantages of the invention may be realized and attained by means of theinstrumentalities, combinations, compositions, and methods particularlypointed out in the appended claims.

SUMMARY OF THE INVENTION

A normalized, adaptive, collaborative matching platform containinginformation associated with a plurality of offerings is combined withinformation of the attributes of a plurality of potential buyers. Therespective listings of articles for sale and potential buyers areiteratively examined, enhanced, normalized, and supplemented to identifypotential matches based on attributes, common characteristics, andlifestyles. Each match is conveyed to respective agents associated withthe buyer/article to foster further examination of a potentialtransaction.

In one version of the present invention a machine implemented methodincludes collecting, for a multiplicity of entities, data (bothstructure and unstructured) which is grouped according to a plurality offactors related to each entity. Using this information one or more tagsare defined wherein each tag is a discrete grouping of the plurality offactors as well as a factor weight or score. These tags are thereafterselectively associated with each of the multiplicity of entities andassigned a weight and confidence so as to derive a lifestyle score. Eachentity may have several lifestyle scores based on a scored relationshipof various associated tags. The platform thereafter matches entitiesbased on a correlation of these lifestyle scores.

Additional features of the methodology describe above can includenormalizing the structured and unstructured data to match a predefinedstructured format criterion and appending the empirical data withthird-party sourced and public data to make it more robust and complete.Appending the data can add ancillary information from these third-partysources and publicly available information as well as identify gaps inthe data itself and data fields that can thereafter be rectified.

The method also allows an agent or client to customize data to enhancethe association of tagging and ultimately matching and to thereafteradapt (refine) the tagging and matching process based on these inputs.During the matching process each factor describes a data characteristicor trait. These factors are grouped and weighed to form tags whichdescribe a plurality of attributes based on empirical data. An agent'seffort to refine the data is rewarded by producing more accurate matchesknown only to the agent and at the same time enables the platform toadapt and refine its normalization, derivation and matching processes soto be more accurate in future endeavors.

Tags, once formed and associated with an entity, are each assigned aconfidence score as to the accuracy of each tag with respect torepresentation by that tag of factors of data related to that tag.Moreover, tags are given a weight as to their significance is assessinga lifestyle score. Agents can modify the factors associated with a tagof an entity thereby refining the factors and tags related to thatentity and thus producing a refined lifestyle score.

The tags associated with each entity, their confidence score and theirweight, provide the basis for determining a lifestyle score for a set ofpredetermined lifestyles. A correlation of these lifestyle scoresbetween assets and individuals forms the basis for a list of matches.

In another embodiment, a non-transitory machine-readable storage mediumcan include machine executable code, which, when executed by at leastone machine, causes the machine to collect and normalize structured andunstructured data for a multiplicity of entities regarding factors thatenable the platform to associate the entity with one or more lifestyles.In doing so, the machine first defines one or more tags based on aplurality of factors and factor weights from the empirical data and thenassociates one or more of these tags with each of the entitles.Lifestyle scores are then derived based on a scored relationship ofassociated tags, tag weights, and tag confidence scores. Finally,entities are matched based on a correlation of lifestyle scores.

A system for adaptive collaborative matching is also presentedcomprising a processor communicatively coupled to a non-transitorystorage medium. The storage medium includes instructions in machineexecutable form which, when executed by the processor, forms theadaptive collaborative matching platform of the present invention.

In one embodiment the adaptive collaborative matching platform includesa normalization engine communicatively coupled to a data store whereinthe data store includes a database having a plurality of data fields ofstructured empirical data and unstructured data for a multiplicity ofentities. The normalization engine converts the unstructured data tostructured empirical data and modifies the structured empirical data toa predefined format. The plurality of data fields of structuredempirical data is thereafter grouped according to a plurality of factorsand each factor is given a weight or score based on the scope of data.

This version of the invention also includes a tag derivation enginecommunicatively coupled to the data store and the normalization enginewherein the tag derivation engine forms a plurality of tags. Each tag isa combination of related factors and each factor is assigned a factorweight. The tag derivation engine also assigns, for each entity, a tagconfidence score for each tag, based on the combination of factors andfactor weights.

A lifestyle engine is communicatively coupled to the data store, thenormalization engine and the tag derivation engine. The lifestyle engineestablishes an entity lifestyle score for each lifestyle of a predefinedset of lifestyles for each entity. Each entity lifestyle score is basedon a combination of tags and a weighted combination of the tagconfidence scores.

Lastly a matching engine is communicatively coupled to the lifestyleengine wherein the matching engine bi-directionally correlates entitiesbased on lifestyles, lifestyle scores, tags and tag scores. Thesematches are communicated to a user via a user interface through acorrelation manager which is configured to present entity matches forwhich the entity lifestyle score for two or more entities exceeds athreshold

The features and advantages described in this disclosure and in thefollowing detailed description are not all-inclusive. Many additionalfeatures and advantages will be apparent to one of ordinary skill in therelevant art in view of the drawings, specification, and claims hereof.Moreover, it should be noted that the language used in the specificationhas been principally selected for readability and instructional purposesand may not have been selected to delineate or circumscribe theinventive subject matter; reference to the claims is necessary todetermine such inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The aforementioned features and other features and objects of thepresent invention and the manner of attaining them will become moreapparent, and the invention itself will be best understood, by referenceto the following description of one or more embodiments taken inconjunction with the accompanying drawings, wherein:

FIG. 1 is high-level diagram illustrating a scope of unknown articles orassets for sale and buyers seeking certain articles, as compared toknown, available articles and potential clients;

FIGS. 2A and 2B depicts differing perspectives of interest in aparticular high-value article as compared to varied interest inhigh-value articles by a particular individual, in accordance with oneembodiment of the present invention;

FIG. 3 shows, according to one embodiment of the present invention, ahigh-level network configuration and communication flow diagram;

FIG. 4 presents an abstract data flow diagram, according to oneembodiment of the present invention;

FIG. 5 is a high-level depiction of a platform for adaptivecollaborative matching, according to one embodiment of the presentinvention;

FIG. 6A is a flowchart of a process, according to one embodiment of thepresent invention, by which to collect and prepare data suitable for useby an adaptive platform for collaborative matching;

FIG. 6B is a flowchart of a process, according to one embodiment of thepresent invention, by which associated and weigh factors defining one ormore tags for use by a platform for adaptive collaborative matching;

FIG. 7 is an expanded flowchart of one methodology, according to thepresent invention, for collaborative matching of high-value articles forsale with potential buyers;

FIG. 8 is a high-level depiction of the architecture for an adaptiveplatform for collaborative matching according to one embodiment of thepresent invention; and

FIG. 9 is a flowchart for communication among correlated entitiesmatched the adaptive collaborative matching platform of the presentinvention.

The Figures depict embodiments of the present invention for purposes ofillustration only. One skilled in the art will readily recognize fromthe following discussion that alternative embodiments of the structuresand methods illustrated herein may be employed without departing fromthe principles of the invention described herein.

DESCRIPTION OF THE INVENTION

An adaptive collaborative platform applies various machine learningtechniques to bi-directionally correlate potential purchasers withhigh-value articles or property that may be of interest. Attributes,characteristics, preferences, and the like of a potential purchaser arescored against attributes and features of articles. The platform of thepresent invention learns from interaction by agents with potentialpurchasers to become more attuned to the desires and lifestyle ofpurchasers and to gain more and more pertinent information from listingagents regarding high-value articles, so as to ultimately to arrive at abetter match between a high value article for sale and a likelypurchaser.

Data from a multiplicity of sources (structured and unstructured) isgathered, normalized and categorized to form, a lifestyle score for eachentity. A matching process is thereafter undertaken to correlate alifestyle preference of a potential purchaser with lifestyle attributesof high-value articles, and to correlate lifestyle attributes ofhigh-value articles with those of potential purchasers.

FIG. 1 presents a graphical depiction of the compartmentalized nature ofinformation and how the collaborative matching platform of the presentinvention pulls these relative silos of information together. In eachlocal market there is a certain degree of understanding of high-valueassets, articles or property that are known to be available forpurchase, P_(a) 110. Similarly, individuals (clients) who are activelylooking to purchase certain types of articles make themselves known,C_(a) 120. But in each market a vast number of articles, P_(b) 130exist, as does a vast number of potential purchasers, C_(b) 140 that areunknown yet would be interested in a transaction of some sort if certainconditions are met. The current state of the art has little ability toidentify and interact with these potential articles for sale orpotential purchasers. As mentioned above, the markets and relatedinformation are siloed and non-collaborative. The present inventiondraws these groups together so that not only are numerous markets awareof active purchasers and assets known to be for sale but provides theability to identify entities that are likely to become active whencertain conditions are present.

Embodiments of the present invention are hereafter described in detailby way of example with reference to the accompanying Figures. Althoughthe invention has been described and illustrated with a certain degreeof particularity, it is understood that the present disclosure has beenmade only by way of example and that numerous changes in the combinationand arrangement of parts can be resorted to by those skilled in the artwithout departing from the spirit and scope of the invention.

The following description with reference to the accompanying drawings isprovided to assist in a comprehensive understanding of exemplaryembodiments of the present invention as defined by the claims and theirequivalents. It includes various specific details to assist in thatunderstanding but these are to be regarded as merely exemplary.Accordingly, those of ordinary skill in the art will recognize thatvarious changes and modifications of the embodiments described hereincan be made without departing from the scope and spirit of theinvention. Also, descriptions of well-known functions and constructionsare omitted for clarity and conciseness.

The terms and words used in the following description and claims are notlimited to the bibliographical meanings, but, are merely used by theinventor to enable a clear and consistent understanding of theinvention. Accordingly, it should be apparent to those skilled in theart that the following description of exemplary embodiments of thepresent invention are provided for illustration purpose only and not forthe purpose of limiting the invention as defined by the appended claimsand their equivalents.

By the term “substantially” it is meant that the recited characteristic,parameter, or value need not be achieved exactly, but that deviations orvariations, including for example, tolerances, measurement error,measurement accuracy limitations and other factors known to those ofskill in the art, may occur in amounts that do not preclude the effectthe characteristic was intended to provide.

Like numbers refer to like elements throughout. In the figures, thesizes of certain lines, layers, components, elements or features may beexaggerated for clarity.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. Thus, for example, reference to “a component surface”includes reference to one or more of such surfaces.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

For the purpose of the present invention the following understandingsare applied.

Agent—An agent is an individual, broker, brokerage firm, or similarentity acting on the behalf of another person or entity. In the instantapplication an agent takes an active role to characterize a person'saffinities, likes and dislikes with respect to a particular type ofproperty or asset, as well as providing key information regardingcertain articles that may be for sale that would be informative tocertain individuals.

Client—A client is an individual or organization using the professionalservices of another. A client in this this instance may list theirproperty with an agent having access to the collaborative matchingplatform of the present invention. Similarly, a client may engage anagent to identify articles of interest using the collaborative matchingplatform.

Asset, Article or Entity—An asset, entity or high-value article is anitem which is or may be for purchase and is characterized by thecollaborative matching platform of the present invention as fitting aparticular lifestyle based on several attributes or tags.

Lifestyle—A lifestyle is a term used in the present invention as ameasure of way of life or behavioral pattern. Various characteristicsidentify an entity's affinity or alignment with a certain lifestyle asdoes a person's likes, actions, purchases, and associations. Being anactivist, a nature lover, or a socialite are examples of lifestyles.

Tag—A tag is a grouping of characteristics or factors used to describean attribute of an entity. For example, an outdoor activity tag mayinclude factors such as recent purchases of outdoor gear, passes atparks, participation in or membership in certain outdoor social groupsor societies, etc.

Factor—A factor is a data characteristic or commonality by which tocharacterize structured data.

Structured data—Structured data are clearly defined making it easilysearchable and resides within a fixed field of a record or file.

Unstructured data—Unstructured data are undefined and not easilysearched such as audio files, video, social postings and the like.Unstructured data has internal structure but is not structured viapre-defined data models or schema. It may be textual or non-textual, andhuman- or machine-generated.

Unless otherwise defined above, all terms (including technical andscientific terms) used herein have the same meaning as commonlyunderstood by one of ordinary skill in the art to which this inventionbelongs. It will be further understood that terms, such as those definedin commonly used dictionaries, should be interpreted as having a meaningthat is consistent with their meaning in the context of thespecification and relevant art and should not be interpreted in anidealized or overly formal sense unless expressly so defined herein.Well-known functions or constructions may not be described in detail forbrevity and/or clarity.

It will be also understood that when an element is referred to as being“on,” “attached” to, “connected” to, “coupled” with, “contacting”,“mounted” etc., another element, it can be directly on, attached to,connected to, coupled with or contacting the other element orintervening elements may also be present. In contrast, when an elementis referred to as being, for example, “directly on,” “directly attached”to, “directly connected” to, “directly coupled” with or “directlycontacting” another element, there are no intervening elements present.It will also be appreciated by those of skill in the art that referencesto a structure or feature that is disposed “adjacent” another featuremay have portions that overlap or underlie the adjacent feature.

Spatially relative terms, such as “under,” “below,” “lower,” “over,”“upper” and the like, may be used herein for ease of description todescribe one element or feature's relationship to another element(s) orfeature(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of a device in use or operation in addition to theorientation depicted in the figures. For example, if a device in thefigures is inverted, elements described as “under” or “beneath” otherelements or features would then be oriented “over” the other elements orfeatures. Thus, the exemplary term “under” can encompass both anorientation of “over” and “under”. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptors used herein interpreted accordingly. Similarly, the terms“upwardly,” “downwardly,” “vertical,” “horizontal” and the like are usedherein for the purpose of explanation only unless specifically indicatedotherwise.

The collaborative matching platform of the present invention breaks downbarriers of the high-value asset sales model to offer a collaborativeplatform for the collection, sharing, enrichment, personalization andvalidation of information. Rather than simply scraping the Internet orother publicly available sources of information, the present inventioninvites disperse and dissimilar agents, brokerage houses, firms and thelike to share information related to both articles for sale andpotential buyers. As available information from each entity may differand be provided in different formats, the present invention works tonormalize and cleanse the information, examine the data for gaps, andthereafter query the provider to supply sufficient data so as to beuniversally collaborative. The invention also seeks additionalinformation to augment that which has been provided to form a moreaccurate depiction of each entity.

The present invention spans multiple market dynamics including language,culture, interests, lifestyle, etc. to provide useful and actionabledata. For example, the process for listing an estate for sale in Japanmay include several features that are normally provided for the Japanesemarket, such as distance from the nearest mass transit station (whichmay be an important factor in determining in the Japanese market, butnot other markets). Likewise, a property listed for sale in Germany maynormally include several attributes that buyers in Germany value, suchaccessibility to the autobahn. Similarly, a buyer from New York mayvalue how far a property is from the nearest airport or a green-spacepark or access to the harbor. Each of these local markets fail toconsider and provide information that is relevant to buyers outsidetheir local area.

One embodiment of the present invention collects, normalizes andaggregates data about articles for sale and individuals who may beinterested in such articles to form accurate and universally usefulinformation regarding each entity as it would apply to one or morelifestyles. Not only is collected data normalized as to language, unitsof measure, and the like, but it is also normalized as to its contentchanging unstructured data to a structured format. Once data issubmitted to the collaborative matching platform it is analyzed for gapsagainst preexisting lists of attributes, traits and characteristicscalled factors. Queries are issued to the supplying agent and/or clientfor the collection of additional information. Gaps in the data arefilled by third-party and public sources and finally derived data,information-based data that already exists, is added to or associatedwith each entity. Upon gaining a certain degree of data with respect toan entity, the information is published for wide dissemination. Data isgrouped according various factors.

One feature of the invention is that the data and the means by which itis collected and normalized is continually refined and enriched based onfeedback, observed behaviors and changing preferences. As requests formore information are gained on an article or property or individual,data fields are created, adjusted, and enriched, new data derived forexisting information is added, and that data is appended withthird-party data to ultimately arrive at a workable set of information.The collaborative database of the present invention gathers, normalizes,and aggregates observed data, derived data, appended data, enricheddata, and, of course, original (agent provided) data. As part of theoverall platform, tools facilitate the process of the data collectionand validation. The invention recognizes that feedback can be used torefine the data collection and normalization process as well as otheraspects of the collaborative matching platform.

Certainly, private details with respect to a potential purchaser aresecured and remain confidential, but certain tastes, preferences,attributes, characteristics and affinities are input into the platformto create a profile. Information such as income bracket, sports likesand dislikes, political affiliations, career fields, familydemographics, and the like are included and modified into a structuredformat to assist the invention understand what makes potential purchaserX tick and what sort of asset would be of interest to purchaser X. Aspart of the overall platform, tools facilitate the process of the datacollection, normalization and validation.

The collaborative platform of the present invention then applies variousmachine learning techniques to extract structured information fromunstructured data and identify common characteristics. Thesecharacteristics are attributed to tags which are used to assess alifestyle. From this information, attributes, characteristics,preferences, etc. of one entity is scored against the attributes andfeatures of another entity to arrive at a match.

For example, assume a potential purchaser has placed certain informationrelating to her preferences for a new property in the collaborativeplatform of the present invention. While only the agent she is workingwith knows her personal information, her profile on the platform issufficient to identify several potential properties which appear to be agood match to her lifestyle. Looking at the matched properties thepotential purchaser seeks additional information, for example, is therea park nearby or is the property bright and sunny. Inquiries arecertainly made back to the listing agent or through public sources torespond to the inquiry, but the collaborative matching platform learnsfrom this purchaser's question and notes that a nearby park is ofinterest to her as are properties that are bright and sunny. Her profileis updated to provide a better match. A property that was beforedismissed as being a marginal match may now be viable since it islocated in close proximity to a park with an open sunny floorplan, andother properties that may appear likely are supplemented by the systemwith information relating to the proximity of a park or have an open andbright floorplans. In that way the potential purchaser is gaining theinformation she needs to make a decision. But the present invention goesa step further. The invention described herein looks at these and otherfactors to assess a lifestyle with these preferences and to modify theprocess by which lifestyles in the future are assessed. Assume in thiscase that the reason the individual wanted to be close to a park andhave a bright and sunny floorplan is that they are very athletic and runin the park often and routinely do yoga in the comfort of their home.This knowledge of a “lifestyle” will further refine the present searchand future like searches.

The present invention iteratively updates and modifies its matchingprocesses, criteria and the profiles on the data it retains in itsdatabase. This data is updated and modified both based on comments froma perspective entity but also based on input from third parties, such asagents.

The present invention reaches beyond active listings of articles forsale and active buyers. Certainly, properties that are currently forsale and buyers actively looking to purchase are included in theplatform creating a bi-directional matching system.

The present invention recognizes that many transactions take placewithout any sort of active listing or search process. In many instancesa friend of a friend knows of a of a property or an article that may befor sale if the price is right, or a friend knows a friend that may beinterested in buying an investment property or article of interest is itmeets their specific interests. These pocket listings or soft buyers arenot represented in the current listings, but they are a vital portion ofthe present invention.

FIG. 2A is a graphic representation of the universe of potentialpurchasers for a high-value article. In this instance, the high-valuearticle 200 is a house or an estate but as one of ordinary skill in therelevant art will appreciate the article may be a yacht, an aircraft, apiece of art, land or collectable item. Two individuals 210, 220 haveidentified themselves to a listing agent 240 as being interested in anarticle of this type 200 and a third individual 230 has conveyed hisinterest through a mutual friend 235. Other individuals 250, 260, 270would be interested but for the fact they not aware the asset is forsale. And yet others 280, 285, 290 are aware that the property 200 isavailable but lack a full understanding of its attributes and may becomeinterested if certain features are present. The collaborative matchingplatform of the present invention goes beyond linking assets for salewith known purchasers, but rather identifies individuals who are likelyto be interested in the property had they only been informed it wasavailable.

Likewise, and as shown in FIG. 2B, the present invention identifies notonly properties that are currently known to be for sale 205 by an agent245, but ones 215, although not currently for sale, in which the ownerhas indicated he or she may be open to selling the property if the pricewas right. The invention also identifies property for sale 225 unknownto the agent, but which may be desirable to a certain class of buyer.Experienced agents are well aware that these types of transactionshappen frequently, but they only occur through extremely protectedrelationships that are rarely communicated outside of a local office.The present invention is therefore bi-directional in that it identifiesor matches buyers to assets rather than simply identifying assets thatalign to a buyer's interests.

The present invention identifies such matches and signals agentspossessing these relationships with information of a potential matchwhile still protecting that coveted relationship. The collaborativematching platform provides information necessary to facilitate a furtherconfidential conversation between agents and clients. In many instancesthe sale may not occur but without the collaborative platform of thepresent invention, the purchaser would not be aware that a covetedproperty in a distant location may be obtained, nor may an owner realizethat a purchaser may indeed exist and be willing to pay that “rightprice” had they only known such an asset existed.

The present invention uses a personality (lifestyle)-based algorithmthat correlates the attributes and features of one entity with thelikes, dislikes, attributes and features associated with another entity,whether or not one entity is an asset actively listed as being availablefor sale, or whether or not another entity is a client actively lookingfor an asset. The present invention provides an agent intelligence andmore personalized ways to engage with property owners and/or the buyersof properties.

FIG. 3 is a data flow diagram of the transfer of information between thecollaborative matching platform of the present invention and variousentities and the adaptive nature of the platform itself. As depicted,the adaptive collaborative platform 310 is communicatively coupled to awide area network 320 such as the public Internet. Through the Internetthe collaborative matching platform, in one embodiment, gains data withrespect to client inquiries 360 from clients interested in purchasingassets and assets that are available for purchase. For example, an agent330 may provide the platform information regarding a piece of fine artidentifying the artist, the mood, color palette, mindset of the artistif known, history, and other things that may be of interest to an artcollector. Similarly, agents may identify a customer or client,anonymously or not, as someone looking for a certain type of sailingvessel, the type of sailing that person likes to do, crew size, ports ofcall, etc., and that this client is also a collector of fine art. Theplatform gains information related to these entities from public data340 and third-party data 350 to supplement entries and build a profile.

Using a person's interested in purchasing a sailing vessel as anexample, the collaborative matching platform may inquire and gain frompublic information that this individual is avid in sailing circles, hasowned several vessels but has traded them up every 2-5 years for alarger ship and that each ship has housed fine art. The system may alsogain a historical list of ports of call based on harbor master recordsand find that he typically cruises the Caribbean and Mediterranean Seasand appears to have a taste for certain fine art related to nauticalthemes.

Client/agent 331 is an agent that operates on behalf of a client(potential buyer) who typically operates only through an agent. Theinteractions of agent 330 and client/agent 331 with the collaborationplatform are similar.

The present invention aligns the interests and preferences of apotential purchaser with the attributes and characteristics of alisting. With further reference to FIG. 4, as more listings are reviewedand feedback is given and inferences 420 gained, the present inventionrefines 370 the matching process and issues inquires to gain moreinformation on properties, or the purchaser, to return better results.The collaborative matching platform 410 may identify several vesselsthat are currently for sale but also identify a few that meet theclient's interests and needs but are not officially listed as being onthe market. These may not be a typical match but one that reflects onboth the client's 440 interest in sailing and art. For example, aclassic sloop of which a version is depicted in a famous painting.Feedback and queries 470 from the client and/or agent 460 can provideinferences as to what qualities or features of the list of first matchesare more or less important to the client, thereby refining the process.Similarly, agents 460 can provide additional information 480, refiningyet still the process.

FIG. 4 is illustrative of the adaptive nature of the collaborativematching process. A successful transaction tied to a match betweenentities provides feedback that the process correctly identified acorrelation. However, only one transaction can occur while there may beseveral successful correlations. The invention recognizes that the useof natural language processing and other semantic techniques may notaccurately normalize unstructured data to a structured empirical format,nor may the association of factors with certain tags and their weightsbe accurate. Lastly the combination of tags and their confidence scoresforming a lifestyle score may requires adjustment. Feedback from users,agents, clients, transactions and the like, are feed back into thematching platform by which the processes are modified (adapted) toarrive at a more refined and accurate matching process.

The adaptive collaborative matching platform creates a matching modelfor each correlation implementation. The processes, factors, factorweights, tag derivations, instructions and the like are stored as afirst model. Upon recognizing feedback such as a user feedback scoreand/or user input to refine the matching process a new, second matchingmodel is formed having modified the processes of the normalization, tagderivation and lifestyle engines. Again, user feedback scores arecollected and compared to prior models. Trends are extracted andrecognized. If subsequent models produce higher feedback scores showingimproved correlations and adoptions of the matches, the adaptivecollaborative matching platform autonomously adopts new instructionsreflective of the improved processes. The process is iterative andongoing enabling the adaptive collaborative matching platform tocontinually improve and learn from prior matches and additional datacollection.

For example, assume a tag in the matching platform is defined asproximity to nature and such a tag includes 3 factors including distanceto municipal parks, distance to open space, distance to national parks.The platform may initially assign an equal weight to each of thesefactors. Upon receiving a list of matches an agent or similar user mayexamine the allocation of factors to the proximity to nature tag andinclude information related to “green space” and assign a higher weightto municipal parks than to national parks. The present inventionrecognizes and tracks such modifications (model 1 vs. model 2) and uponseeing trends modifies the processes by which the matching platformoperates. In this case, after several independent submittals of feedbackrelating to the proximity to nature tag, the invention may add a fourthfactor and/or vary the factor weights. Again, this modification,refinement, process is iterative and continuous and applies to allaspects of the collaborative matching platform.

Turning back to the earlier purchaser who asked if a property is near apark, the adaptive collaborative matching platform of the presentinvention cannot only reevaluate other properties with known proximityto parks and present those as possible matches, but it also can sendqueries to the listing agent of similar properties to gain informationwith respect to how far are their properties from the nearest park,athletic facilities, yoga studios and the like, that meet thepurchaser's lifestyle. Those that come back with favorable data can beagain evaluated based on the new information.

To accomplish these, and other, goals, the collaborative matchingplatform of the present invention forms tags related to certain commonattributes, characteristics or features (called factors) of theproperties, and, of the potential purchasers (entities). Not all factorsare equal. In some instances, a certain factor may have a more drivingeffect on a tag. And one factor may be used or associated with severaltags but have a different impact on each tag. The collaborative matchingplatform thereafter associates combinations of these tags, along with atag confidence score, with an entity to arrive at a particular lifestylescore. The lifestyle is scored based on characteristics of theirpersonalities, their behaviors, and the like reflected in combined tagswith a measure of confidence that the tags accurately reflect thecharacteristics of that entity.

Each entity may be associated with several tags and each tag may reflectseveral factors such as privacy, social activities, entertaining, andthe like. The factors are weighed and used to craft a score according totheir reliability and validity. A verified public record reflecting thata property is adjacent to an open space may provide high certainty inthis feature's contribution to the outdoor activity tag. Accordingly,that sort of structured data may result in a high factor weight as to anopen space factor weight. A subjective unstructured review of theproperty that simply states, “this property is close to open space” mayreceive a lower confidence rating, even after the unstructured data isresolved to a structured format. For example, “close” may be normalizedto less than 0.5 miles but greater than 0.25 miles. A tag is associatedwith various factors and their weights which results in a degree ofconfidence that the tag represents a certain attribute. Tags are furtherassociated with lifestyles which are based on a combination of tags anda confidence rating. Thus, a score of 75 for an outdoor activities tagis qualified as to a degree of confidence, which is considered by thelifestyle engine when assessing a lifestyle score, such as, nature loverlifestyle.

FIG. 5 is a high-level system architecture for one embodiment of thecollaborative matching platform of the present invention. Thecollaborative matching platform 510 is communicatively coupled to aplurality of data sources 520, clients, agents and third parties whichprovide structured and unstructured data to the platform. As discussed,the platform of the present invention is envisioned as residing on aseparate server and offered as a service. However, having the platformresident on a client location or distributed using a server cluster as ameans to implement the platform are within the scope of the presentinvention.

In the instance shown in FIG. 5, the collaborative matching platform 510resides on a server 530 having a non-transitory storage medium on whichinstructions, in the form of machine executable code, exist. Theseinstructions, when executed by the processors on the server, form aninstantiation of the collaborative matching platform 510 of the presentinvention. The collaborative matching platform 510, as depicted, iscommunicatively coupled to a data store 540. The data store 540 may beresident on the server or within a local area network or securelycoupled to the platform using secure communication techniques such astunneling or encapsulation. These techniques are well known to one ofreasonable skill in the relevant art.

The collaborative matching platform 510 includes, in this embodiment, anormalization engine 550, a tag derivation engine 560, a lifestyleengine 570 and a matching engine 580. As shown the normalization engine550, the tag derivation engine 560 and lifestyle engine 570 are incommunication with each other to arrive at the most accurate assessmentof an entity's lifestyle. The lifestyle engine 570 is thereaftercommunicatively coupled to the matching engine 580 which ultimatelyaligns the lifestyle scores of entities. Upon identification of a matchor series of matches the output is conveyed to a suitable user interface590 for consideration. Users may thereafter provide feedback and revisedata associate with factors, factor association and weights asassociated with tags, and the combination of tags as considered whencrafting scores for one or more lifestyles.

At a high level, data is collected and normalized or cleansed using thenormalization engine. Structured empirical data is input into a databaseand into data fields. Unstructured data is analyzed using naturallanguage processing and semantic analysis to arrive at some form ofstructured data. Gaps in the data are recognized and rectified eitherwith direct inquiry to the supplier of the data or through third-partydata sources. For example, assume that a gap exists in an asset'sdescription such as a property's distance from a park or fitnessfacility. Parks and fitness facility locations are widely available frompublic sources and can be directly queried by the collaborative matchingplatform to determine such information and used to supplement theexisting asset profile. Access to a mapping software or website may beable to ascertain that a property is exactly 0.4 miles from the nearestfitness facility. In another instance a news article may state that theproperty is very close to a fitness facility. This unstructured data maybe interpreted as meaning the property is no more than 0.5 miles awaybut greater than 0.2 miles. Now structured, the data nonetheless has alower degree of confidence that the prior structured example.

Data fields within the data store's database can also be derived. Forexample, historical and public information may determine that a likelypurchaser has previously owned and currently owns a home that is bothclose to a golf course and a beach. Moreover, the owner is an aviddeep-sea fisherman based on public purchases of equipment, postsregarding travel, competitions, and the like. That individual's(entity's) profile is modified by the platform to include data fieldsand accompanying data to reflect an affinity for homes having closeaccess to a deep-water port, boating and golf, even though thosespecific issues were not supplied by the individual.

The information can also be enriched from agent and client input as canthe process by which the data is evaluated. In such an instance a newduplicate but enriched profile is created leading to more preciseresults. The tags associated with this new profile are updated and theresulting output of the matching engine directed to the agent whosupplied the additional information. Importantly, the new input is usedto refine the normalization, tag derivation and lifestyle scoringprocess.

As discussed, the normalization engine of the present invention modifiesthe format of data (structured and unstructured) received from varioussources to align with a common, predetermined format protocol. Thenormalization engine also looks at various data fields for a particularentity and identifies and attempts to resolve gaps in data. Datacollection is typically done at a local level. Cultural norms andexperience in a local market drive the agents and similar personnel togather information appropriate for that local market. However, localdata fields may not accurately address the needed information tocomplete a lifestyle analysis of the present invention. Accordingly, thepresent invention goes beyond simple translation of provided data byanalyzing the data fields or lack thereof. In instances in which thedata provided is missing certain fields of information the platform willseek the information from the providing source, third-party sources andpublic sources to create a robust database of information for eachentity.

Normalization of data can be illustrated by the following example.Assume an individual in San Francisco casually tells a broker that theymay be in the market to buy a ski house in the Rockies. They expresssome likes and dislikes but offer no definitive timeline or geographicrestrictions. The information is input into the present invention whichnormalizes (structures) the data and attempts to fill in gaps such assize, price range, income level, attributes of former or current homes,club affinities, purchases of sporting equipment or other data that mayprovide insight as to the potential purchasers state of mind. Certainaspects of the individual can be ascertained as structured empiricaldata such as age, reported income, marital status, etc. Likewise, afriend of a friend tells a broker in Colorado that an individual inAspen may be interested in selling a second home if the rightopportunity was presented. Some details are listed and in this case thetwo appear to line up, but each data profile is incomplete. Therespective agents are notified and inquires for additional data sent.

In high net-worth markets certain attributes can modify the normal meansby which specific assets are valued and thereafter modify the way aclass of assets are valued. Certain buyers' value different aspects of aproperty differently when they make a purchase. One may value privacymore so than the number of rooms. High value asset valuation does notfollow normal valuation models but is rather more akin to the way aperson lives or the lifestyle opportunities a property may present. Thepresent invention captures criteria of value to one entity and imputesthose to prospective entities possessing those characteristics. Just asbeauty is in the eye of the beholder, an asset's value, in certainmarkets, is strongly influenced by the affinities of the purchaser.

The present invention, in another embodiment, integrates local economictrends and normalizes them. The invention incorporates trends fromrelevant markets (such as art and auto auctions), to value a propertymore as a piece of art rather than just a traditional piece property,and applies statistical techniques that are appropriate for building analgorithm for a segment of homes where the data set is smaller andsparse.

With additional reference to FIG. 6A, a process by which to collect andnormalize data for the collaborative matching platform of the presentinvention is shown. Data with respect to an owner, property or a buyer(entity) is collected 610 and normalized 630 to be structured and in thesame format and protocol if found 620 to be aberrant. Gaps in the dataare recognized 640 and third-party sources are tapped to append 650 theprovided data. Using this information, the data is enriched by expandingthe number of fields 660 with respect to certain attributes and toultimately derive new data. As new data fields and gaps are recognizedthe process repeats 670, looking again for public or third-party data tocreate a better representation of the entity. The data is grouped andweighed according to a plurality of factors 680 and ultimately passed tothe tag derivation engine.

With reference to FIG. 6B, the tag derivation engine 560 receives datafrom the normalization engine 550 and derives 612 a plurality of tags,each describing a lifestyle attribute. The derivation engine groups orassociates 622 collected data according to factors relating to eachentity. Factors that represent evidence, common traits, characteristicsof a particular interest or activity are placed in discrete groups.Factors are based on groupings of data. For example, the number ofpurchases of outdoor gear in the last 6 months may be one factor.Another factor may be the number of subscriptions to an outdoor focusedperiodical. A “likes the outdoors” tag may be based on factors such asthe number of purchases of outdoor gear in the last 6 months, the numberof outdoor focused subscriptions, and the number of visits to nationalparks in the last 5 years. The type and amount of data results in itsgrouping into a factor and results in a factor weight 632. Each entitymay or may not be associated with a particular tag and two or moreentities may be associated with the same tag. The tag however is scored(weighted) differently for each entity 632. For example, two entitiesmay be associated with the “likes the outdoors” tag but one entity mayhave data that reflects several visits to national parks while the othermay have very few visits to national parks but is an avid reader ofoutdoor periodicals.

Once a tag is derived, the tag receives a score indicating the abilityof this particular tag in capturing these types of characteristics foran entity and is associated 642 with that entity. In this case the firstentity may have a high “likes the outdoors” tag confidence based onhighly weighting the number of visits to national parks. The secondentity may receive a lower tag confidence rating since despite being anavid subscriber to outdoor focused periodicals, the entity has hadlimited contact with national parks. The tag combinations and theirconfidence ratings are applied to a score before it is passed to thelifestyle engine. As users review the data, tag derivation and theirweights, feedback is received, and a user may modify 652 particular setsof data, factors, and weights. These modifications are feed back intothe tag derivation process so that subsequent derivations can be moreaccurate and refined.

Returning to the prior example of a home that may be offered for sale inthe Rockies. The collaborative matching platform has gained informationnot only on the specifics of the home such as size, cost, tax base,etc., but also features such as access to open space, hiking trails,distance to ski slopes, light profiles inside the house, distance fromneighbors, distance to schools, distance to the local market, socialopportunities, etc. These factors are grouped and weighed. One tag inthis example may represent outdoor activities. Factors such as proximityto hiking trails, ski slopes, and open space contribute to that tag'sscore. Another tag may relate access to amenities and services. Thedistance to markets, the number of nearby shops, number of bars nearbymay be factors in the amenities and services tag.

Based on collected and normalized data for an entity, the outdooractivity tag may be scored at 75 while the amenities and services tag at25. For another entity the same tags may be scored 35 and 50,respectively. In the first instance, the close proximity to hikingtrails, ski slopes and open space speaks strongly that this entity isaligned with outdoor activities yet may also be associated with anindividual who is self-sufficient and not reliant on service providers.Thus, a measure confidence is assigned to indicate the strength of thesevalues.

The tagging process of the present invention is rule driven usingnatural language processing and the like to craft tags based on searchparameters. Tagging requires cohesive and consistent structured data. Inone version of the present invention, tags are extracted frominformation pertinent to drive characterizing both properties andpotential purchasers. As the present invention gathers more informationabout each property and the preferences of the purchasers it can refinethe matching algorithm and provide a curated presentation ofopportunities. One purchasers' affinity for location or layout tosupport an entertaining lifestyle may drive which properties arepresented, and how they are presented while a similar purchaser havingdifferent interests would experience a completely differentpresentation, tuned to their needs.

The lifestyle engine of the collaborative matching platform examines thecombination of tags, their scores, and the confidence of each score andaligns each with one or more predetermined lifestyles. A lifestyle is abehavior, attitude, core value system, world view, what providespleasure or satisfaction, or simply a way of life or what makes a persontick. Lifestyle may include views on politics, religion, health,intimacy, and more. Individuals may possess several different aspects oftheir lifestyle and certainly the present invention recognizes that aperson on one day may be embracing one side of their personality and dosomething completely different the day after. The present inventioncrafts a measure of a particular lifestyles of both the asset andindividual.

Lifestyles of the present invention may include athletic, nature lover,socialite, entertainer, leisure, adventurist, business or corporate,creative, artistic, activist, technician and the like. Certain tagsalign with certain types of lifestyles. For example, a high scoringoutdoor activity tag would may be aligned with a nature lover andathletic but not as applicable to an entertainer or socialite. But anature lover may or may not be athletic and an individual with anathletic lifestyle may or may not like nature. Accordingly, the tagsprovide inputs to the lifestyle engine to assess a particular entity'slifestyle. For each of the predetermined lifestyles, the entity receivesa value or score. If the lifestyle score exceeds a predefined threshold,the lifestyle and its score is associated with the entity.

Turning back to our individual who has expressed interest in owning ahome in the mountains, the factors and data collected with respect tothat individual have found that they possess a high score outdooractivity tag with high confidence, and perhaps a high score on privacy,albeit with lower confidence. Based on these and other tags theindividual may be associated with nature lover score of X and anathletic score of Y. They would also be assessed a score for the otherlifestyles such as business or corporate, creative, activist and thelike.

Each entity is associated with each tag and each lifestyle of a set oflifestyles is assessed for each entity. As each entity has unique scoreson each tag due to the data collected and the factor weights, eachlifestyle score is different. One entity who has high scores for anurban socialite may have low scores for nature lover.

The matching engine of the collaborative matching platform identifiescorrelations between lifestyle scores by employing machine learning andrelated neural algorithmic processes in the data normalization, tagderivation and lifestyle matching processes. In one embodimentalgorithms embody artificial intelligence and neural networks to modeldata using graphical techniques. Symbolic logic, rules engines, expertsystems and knowledge graphs are used in concert with machine learningto capture otherwise unrealized identifiers in the data. The presentinvention modifies itself when exposed to more data. It is dynamic anddoes not require human intervention to make certain changes. Elements ofthe weighted approach include a quality indicia (Qi) which is thepresence of a given characteristic associated with the client or theasset. It also includes weight indicia (wi)—this is a weight assigned toa given quality in the creation of a matching profile based oncharacteristics impact or importance; and lastly a confidence level (ci)which is a rating of confidence in the assignment of the quality to agiven person or property.

The essence of the present invention is that an individual is morelikely to be interested in and purchase an asset that is aligned withtheir lifestyle. The system characterizes an asset as being aligned withcertain lifestyles and then seeks individuals who share those behavioralorientations, vice versa, the invention identifies the behavioralorientation of an individual and finds assets that are so aligned.

These processes described herein overlap and occur concurrently,iteratively and in real time and are designed to be internally adaptive.The invention recognizes feedback as how to improve and autonomouslyrevises the algorithmic process to improve accuracy. As more informationis entered and gathered the process becomes more precise and moresuccessful in its ability to match properties with a purchaser'saffinities.

FIG. 7, which provides a flowchart of a methodology for adaptivecollaborative matching according to one embodiment of the presentinvention, can be better understood when viewed in combination with FIG.8. FIG. 8 is a high-level architecture of the adaptative collaborativematching process of the present invention. The process begins 705 withthe collection 710 and normalization of data 810 for each entity 830. Indoing so a multiplicity of inquiries are made to determine if thecorrect fields are included for the data and to gain information tosupplement the original data entries. The data is categorized (grouped)according to factors which are thereafter assigned a weight based ondepth of that data related to that factor and how it relates to a tag.

From the data gained, factors 815 are grouped 720 forming tags 820representative of certain lifestyle attributes. Each factor is assesseda weight 825. Data with respect to factors such as travel purchases,club memberships, and the like may find associations into a certain typeof likes to travel tag which is associated 730 with each entity. Forexample, a tag for “outdoor activities” may heavily rely on and weigh afactor for outdoor subscriptions. A “leisure travel” tag may alsoconsider the outdoor subscription tag but not weigh it as high as afactor for airline ticket purchases. Likewise, the tag for “outdoor”activities may not even consider the airline ticket purchase factor. Thefactors are weighed 825 and the data is assessed to craft a tag score835 as well as a tag degree of confidence for each entity 830. That is,how confident, based on the data for that entity, is the tagrepresentative of a particular attribute. The same tag for one entitymay have a very high degree of confidence, while for another entity thedata behind the tag does not convey as much confidence.

From the tags associated with each entity, the lifestyle engine 570defines 740 and assesses 750 a lifestyle score 860 for each lifestyle850 based on combinations of tags, tag scores and confidence ratings ofthose tag scores. Each entity 830 may possess a number of differentlifestyle scores 860 to arrive at a unique overall impression of thatpersons or assets behavioral characteristics. For example, a person maybe an activist who loves nature and is athletic. Another may be asocialite who loves to entertain but appears to be very involved inactivist groups for ecology, conservation and nature. Similarly, assetsmay possess traits or characterizes that are aligned with suchlifestyles. A home in the mountains may be more aligned with a naturelover than a social activist, yet a suburban condominium with closeaccess to know public venues may fit of the activist who likes toentertain. Conversely, a home in the mountains in an activist or liberalleaning community may be more attractive than the same property in aconservative right leaning region.

Each lifestyle is scored for each entity and provided with a measure asto how confident the platform is with its assessment. Using a weightingrule-based approach the lifestyles of the entities are correlated toidentify matches 760 between entities. Feedback is obtained 770, newdata is sought, collected, normalized, derived and applied 780. Tags arere-associated, their evaluations reassessed, and lifestyles are onceagain measured and valued. The iterative process of the collaborativematching platform enables clients and agents alike to develop a precisemarketing profile 785 for a particular asset so as to look forindividuals who possess the lifestyle that would find the assetinteresting or aligned with their interests. It also enables theplatform to refine its processes so that the next set of matches aremore accurate and applicable.

A significant feature of the present invention is agent engagement. Thefront end of the platform is a dashboard through which the agent caninteract. It is a system by which an agent can identify new leads, leadsbeyond those that exist in their current brokerage. The leads can beranked or scored based on the degree of matching (correlation) topresent to the agent a measure of what avenue to pursue first. Matchesare listed but also ranked. Recall that currently a brokerage within onecommunity would not know of what is on the market and associated withanother brokerage, despite the fact that such information would directlymeet a purchaser's needs. Information is currently broadcasted but notcorrelated. The invention provides personalized recommendations betweenagents so that agent A listing a property in city B, can become aware ofa purchaser that exists in city C represented by agent D, and viceversa.

The platform also promotes and rewards agents for refining informationrelated to an entity. As matches are reviewed, the platform will seekadditional publicly available or third-party provided information. Anagent working with a client or an asset can proactively seek and gainsuch information to make the matching process more accurate. Theinformation can be refined, information added or deleted based on theagent's knowledge of the client. The present invention isolates theagent's efforts to a new data file so that only that agent can see therefined matches. Accordingly, an agent willing to expend time and effortto assist in data collection and tag assessment is rewarded with moreaccurate and on point matches.

Another feature of the present invention is privacy and security. Oneaspect of the platform resides on top of existing data that alreadyexist and resides at various agents and brokers. For example, in thereal estate market, current brokerage houses possess a database ofproperties and client profiles. Clearly such information is proprietary,and collaboration of the data raises concern of loss of suchinformation.

The present invention shares behavioral information, attributes, andcharacteristics of both properties and potential buyers withoutproviding data that would undermine individual brokerage operations orbreaching their confidential information. The present invention enablesagents to trust the platform and create a unique database reachingbeyond geographic boundaries that drives engagement rather thancompartmentalization.

Included in the description are flowcharts depicting examples of themethodology for collaborative matching as described above. In thisdescription, it will be understood that each block of the flowchartillustrations, and combinations of blocks in the flowchartillustrations, can be implemented by computer program instructions.These computer program instructions may be loaded onto a computer orother programmable apparatus to produce a machine such that theinstructions that execute on the computer or other programmableapparatus create means for implementing the functions specified in theflowchart block or blocks. These computer program instructions may alsobe stored in a computer-readable memory that can direct a computer orother programmable apparatus to function in a particular manner suchthat the instructions stored in the computer-readable memory produce anarticle of manufacture including instruction means that implement thefunction specified in the flowchart block or blocks. The computerprogram instructions may also be loaded onto a computer or otherprogrammable apparatus to cause a series of operational steps to beperformed in the computer or on the other programmable apparatus toproduce a computer implemented process such that the instructions thatexecute on the computer or other programmable apparatus provide stepsfor implementing the functions specified in the flowchart block orblocks.

Accordingly, blocks of the flowchart illustrations support combinationsof means for performing the specified functions and combinations ofsteps for performing the specified functions. It will also be understoodthat each block of the flowchart illustrations, and combinations ofblocks in the flowchart illustrations, can be implemented by specialpurpose hardware-based computer systems that perform the specifiedfunctions or steps, or combinations of special purpose hardware andcomputer instructions.

FIG. 9 is a flowchart for communication among correlated entitiesmatched the adaptive collaborative matching platform of the presentinvention. Upon determining 910 that a correlation exists between two ormore entities based on one or more lifestyle scores exceeding apredefined threshold, the correlation manager 585 crafts 920 anelectronic message or the like to communication agents informing themthat two or more entities have matched. Recall that the lifestyle scoreis calculated from a set of tags, tag scores, and tag confidence scoresfor each entity and each tag is based on structured data grouped byfactors, each factor being assigned a factor weight forming a firstmatching model.

The communication agent and the correlation manager 585 operated inconjunction with the user interface 590 to gain user feedback scoring930 regarding the current matching model. Based on the feedback and thefeedback scores, a second model is formed 940 and implemented by theadaptive collaborative matching platform. If matches improve validatedby feedback or transactional data, modifications are implemented makingthe second model, the primary or first model. Upon doing so the data isreassessed and new correlations 910 are identified.

The invention also tracks successful correlations based on transactionaldata 935. As matches occur and are communicated to users one or moretransactions may take place validating that the match indeed wassuccessful. Similarly, offers may also indicate successful correlationsand matches. This information, or lack thereof, is used to “score” thematching model and thereafter modify the instructions to create bettermatches in the future.

Some portions of this specification are presented in terms of algorithmsor symbolic representations of operations on data stored as bits orbinary digital signals within a machine memory (e.g., a computermemory). These algorithms or symbolic representations are examples oftechniques used by those of ordinary skill in the data processing artsto convey the substance of their work to others skilled in the art. Asused herein, an “algorithm” is a self-consistent sequence of operationsor similar processing leading to a desired result. In this context,algorithms and operations involve the manipulation of informationelements. Typically, but not necessarily, such elements may take theform of electrical, magnetic, or optical signals capable of beingstored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” “words”, or the like.These specific words, however, are merely convenient labels and are tobe associated with appropriate information elements.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

It will also be understood by those familiar with the art, that theinvention may be embodied in other specific forms without departing fromthe spirit or essential characteristics thereof. Likewise, theparticular naming and division of the modules, managers, functions,systems, engines, layers, features, attributes, methodologies, and otheraspects are not mandatory or significant, and the mechanisms thatimplement the invention or its features may have different names,divisions, and/or formats. Furthermore, as will be apparent to one ofordinary skill in the relevant art, the modules, managers, functions,systems, engines, layers, features, attributes, methodologies, and otheraspects of the invention can be implemented as software, hardware,firmware, or any combination of the three. Of course, wherever acomponent of the present invention is implemented as software, thecomponent can be implemented as a script, as a standalone program, aspart of a larger program, as a plurality of separate scripts and/orprograms, as a statically or dynamically linked library, as a kernelloadable module, as a device driver, and/or in every and any other wayknown now or in the future to those of skill in the art of computerprogramming. Additionally, the present invention is in no way limited toimplementation in any specific programming language, or for any specificoperating system or environment. Accordingly, the disclosure of thepresent invention is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

In a preferred embodiment, the present invention can be implemented insoftware. Software programming code which embodies the present inventionis typically accessed by a microprocessor from long-term, persistent,non-transitory, storage media of some type, such as a flash drive orhard drive. The software programming code may be embodied on any of avariety of known media for use with a data processing system, such as adiskette, hard drive, CD-ROM, or the like. The code may be distributedon such media or may be distributed from the memory or storage of onecomputer system over a network of some type to other computer systemsfor use by such other systems. Alternatively, the programming code maybe embodied in the memory of the device and accessed by a microprocessorusing an internal bus. The techniques and methods for embodying softwareprogramming code in memory, on physical media, and/or distributingsoftware code via networks are well known and will not be furtherdiscussed herein.

Generally, program modules include routines, programs, objects,components, data structures and the like that perform particular tasksor implement particular abstract data types. Moreover, those skilled inthe art will appreciate that the invention can be practiced with othercomputer system configurations, including multi-processor systems,microprocessor-based or programmable consumer electronics, network PCs,minicomputers, mainframe computers, and the like. The invention may alsobe practiced in distributed computing environments where tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

While there have been described above the principles of the presentinvention in conjunction with a collaborative matching platform, it isto be clearly understood that the foregoing description is made only byway of example and not as a limitation to the scope of the invention.Particularly, it is recognized that the teachings of the foregoingdisclosure will suggest other modifications to those persons skilled inthe relevant art. Such modifications may involve other features that arealready known per se and which may be used instead of or in addition tofeatures already described herein. Although claims have been formulatedin this application to particular combinations of features, it should beunderstood that the scope of the disclosure herein also includes anynovel feature or any novel combination of features disclosed eitherexplicitly or implicitly or any generalization or modification thereofwhich would be apparent to persons skilled in the relevant art, whetheror not such relates to the same invention as presently claimed in anyclaim and whether or not it mitigates any or all of the same technicalproblems as confronted by the present invention. The Applicant herebyreserves the right to formulate new claims to such features and/orcombinations of such features during the prosecution of the presentapplication or of any further application derived therefrom.

We claim:
 1. A system for adaptive entity matching, comprising: aprocessor communicatively coupled to a non-transitory storage mediumhaving instructions in machine executable form which, when executed bythe processor, forms an adaptive collaborative platform, the platformincluding a normalization engine communicatively coupled to a data storewherein the data store includes a database having a plurality of datafields of structured empirical data and wherein the normalization enginemodifies unstructured data into the structured empirical data usingnatural language processing and thereafter groups the plurality of datafields of the structured empirical data according to a plurality offactors, each factor being basis for a discrete grouping of thestructured empirical data; a tag derivation engine communicativelycoupled to the data store and the normalization engine wherein the tagderivation engine forms a plurality of tags, each tag being acombination of the plurality of factors and wherein each factor of theplurality of factors is assigned a factor weight, and wherein the tagderivation engine assigns, for each entity, a tag score and a tagconfidence score for each tag, based on a combination of the structuredempirical data, the plurality of factors and the factor weights of eachof the plurality of factors, a lifestyle engine communicatively coupledto the data store, the normalization engine and the tag derivationengine, wherein the lifestyle engine establishes, for an entity, anentity lifestyle score for each lifestyle of a predefined set oflifestyles and wherein each of the entity lifestyle scores is based on acombination of the plurality of tags, a weighted combination of theassigned tag scores, and the assigned tag confidence scores, and amatching engine communicatively coupled to the lifestyle engine whereinthe matching engine iteratively correlates the entities based onlifestyles, the entity lifestyle scores, the plurality of tags and theassigned tag scores forming a first matching model; and a user interfacecommunicatively coupled to the processor configured to send a usermessage listing entity correlations based on the first matching model,and to receive a user feedback score for the entity correlations andwherein based on the user feedback score the matching engine, thelifestyle engine, the tag derivation engine and the normalization enginemodify instructions and form a second matching model.
 2. The system foradaptive entity matching according to claim 1, wherein responsive to auser feedback score of the second matching model exceeding the userfeedback score of the first matching model, the adaptive collaborativeplatform autonomously adopts instructions associated with the secondmatching model.
 3. The system for adaptive entity matching according toclaim 1, wherein responsive to user feedback scoring of the firstmatching model, the adaptive collaborative platform alters the factorweights of each of the plurality of factors and the assigned tag scoresin forming the second matching model.
 4. The system for adaptive entitymatching according to claim 1, whereby the second matching modelincludes a modified plurality of factors and a modified lifestyle scorefor each of the lifestyles for each of the entities based on the userfeedback.
 5. The system for adaptive entity matching according to claim1, wherein the adaptive collaborative platform autonomously modifies theassignment of the factor weight to each of the plurality of tags basedon recognized subsequent user modifications.
 6. The system for adaptiveentity matching according to claim 1, wherein each of the first matchingmodel and the second matching model is a correlation implementation andwherein the adaptive collaborative platform collects, stores andcompares the user feedback score for each of the first matching modeland the second matching model and identifies trends for improvedcorrelations and match adoptions.
 7. The system for adaptive entitymatching according to claim 6, wherein responsive to identifying trendsfor improved correlations and match adoptions, autonomously adopting newinstructions reflective of improved correlation implementations.
 8. Thesystem for adaptive entity matching according to claim 1, wherein thenormalization engine, the tag derivation engine and the matching engineemploy machine learning and neural algorithmic processes.
 9. A machineimplemented method for entity correlation communication, comprising:modifying, by a normalization engine, unstructured data retrieved from adata store having a plurality of data fields into structured empiricaldata using natural language processing; forming a plurality of tags, bya tag derivation engine, each tag being a combination of a plurality offactors and wherein each factor of the plurality of factors is assigneda factor weight and a tag score; establishing, by a lifestyle engine, anentity lifestyle score for each lifestyle of a predefined set oflifestyles and wherein each of the entity lifestyle scores is based on acombination of the plurality of tags and the assigned tag scores;determining, by a matching engine, a correlation between two or moreentities when one or more of the lifestyle scores of each of the two ormore entities exceed a correlation threshold, wherein each of the one ormore lifestyle scores is calculated from a set of tags, the assigned tagscores, and tag confidence scores for each of the two or more entitiesand wherein each tag of the plurality of tags is based on the structuredempirical data grouped by the plurality of factors, each factor of theplurality of factors being assigned a factor weight forming a firstmatching model; transmitting, by a correlation manager, an electronicmessage to one or more communication agents, wherein each entity of thetwo or more entities is associated with at least one communicationagent, the electronic message signaling that correlation between the twoor more entities exceeded the correlation threshold based on the firstmatching model; receiving, through a user interface, from the one ormore communication agents, user feedback scoring of the first matchingmodel; and forming a second matching model based on the user feedbackscoring and thereafter modifying one or more of the matching engine, thelifestyle engine, the tag derivation engine or the normalization engine.10. The machine implemented method according to claim 9, furthercomprising grouping the structured empirical data according to theplurality of factors.
 11. The machine implemented method according toclaim 9, wherein the forming the plurality of tags includes assigning atag confidence score for each tag based on a combination of thestructured empirical data, the plurality of factors and the factorweights of each of the plurality of factors.
 12. The machine implementedmethod according to claim 11, wherein the entity lifestyle scoreincludes a weighted combination of the assigned tag scores and theassigned tag confidence scores.
 13. The machine implemented methodaccording to claim 9, wherein responsive to receiving user feedbackscoring of the second matching model exceeding the user feedback scoringof the first matching model, adopting, by the matching engine;modifications associated with the second matching model.
 14. The machineimplemented method according to claim 9, further comprising modifying,by a user through the user interface, one or more factors associatedwith a tag of an entity thereby forming the third matching model with arefined plurality of factors related to that entity and a refinedlifestyle score.
 15. The machine implemented method according to claim9, wherein each of the first matching model and the second matchingmodel represents a correlation implementation and further comprisingcollecting, storing and comparing the user feedback score for each ofthe first matching model and the second matching model and thereafteridentifying trends for improved correlations and match adoptions. 16.The machine implemented method according to claim 15, wherein responsiveto identifying trends for improved correlations and match adoptions,autonomously adopting new instructions reflective of improvedcorrelation implementations.
 17. The machine implemented methodaccording to claim 9, wherein determining the correlation employsmachine learning and neural algorithmic processes.